Dual-Attention-Driven Multiscale Fusion Object Searching Network for Remote Sensing Imagery

Abstract

Object search is a challenging yet important task. Many efforts have been made to address this issue and achieve great progress in natural image, yet searching all specified types of objects from remote sensing image is barely studied. In this work, we are interested in searching objects from remote sensing images. Compared to person search in natural scenes, this task is challenging in two factors: One is that remote image usually contains a large number of objects, which poses a great challenge to characterize the object features. Another is that the objects in remote sensing images are dense, which easily yields erroneous localization. To address these issues, we propose a new end-toend deep learning framework for object search in remote sensing images. First, we propose a multi-scale feature aggregation (MSFA) module, which strengthens the representation of lowlevel features by fusing multi-layer features. The fused features with richer details significantly improve the accuracy of object search. Second, we propose a dual-attention object enhancement (DAOE) module to enhance features from channel and spatial dimensions. The enhanced features significantly improve the localization accuracy for dense objects. Finally, we built two challenging datasets based on the remote sensing images, which contains complex changes in space and time. The experiments and comparisons demonstrate the state-of-the-art performance of our method on the challenging datasets.